Radiology is the branch of medical science dealing with medical imaging for the purpose of diagnosis and treatment. The practice of radiology often involves the usage of X-ray machines or other radiation devices to perform the diagnosis or administer the treatment. Other practices of radiology employ techniques that do not involve radiation, such as magnetic resonance imaging (MRI) and ultrasound. Within the medical field, radiology can refer to two sub-fields, diagnostic radiology and therapeutic radiology. Diagnostic radiology is concerned with the use of various imaging modalities to aid in the diagnosis of disease or condition. Therapeutic radiology or radiation oncology uses radiation to treat diseases such as cancer through the application of radiation to targeted areas.
In diagnostic radiology, a typical configuration for a radiology device includes a radiation source used to emit the irradiating particles (e.g., X-rays) and one or more imaging devices corresponding to the radiation source. The particles are directed towards a target volume (i.e., a patient) situated between the source and imaging device(s), with the imaging device(s) being positioned to collect incoming radiation that passes through the target volume. The beams collected by the imagers are subsequently used to generate one or more images of the targeted volume. In radiation therapy, accurately identifying and delineating anatomic structures during the treatment planning phase is critically important. The objective of every such procedure is to provide an accurate definition of a target volume and any organs at risk in order to deliver the maximum radiation dose to the target volume (e.g., tumor) while sparing the surrounding healthy tissue from being subject to exposure to potentially harmful radiation.
During the planning stage, a planner often defines specific structures used to control the dose distribution during treatment optimizations. Typically, these structures are Boolean combinations of targets and normal tissues. By defining the intersection of targets and normal tissues as separate structures, different prescription doses and constraints can easily be applied to different regions, facilitating the creation of controlled dose gradients between normal tissues and targets.
In biological radiation planning, planning metrics are typically expressed according to the biological effect of the irradiation. In contrast, in traditional (non-biological) radiation planning, the measure of an absorbed radiation dose is typically expressed as a physical quantity that does not take into account biological context. The lack of biological context when considering the application of an absolute dose becomes an issue when the effects inherent to radiation application in different fraction doses need to be summed together or compared with each other. Recently, this problem has become more prevalent, since an increasing number of radiation subjects (e.g., treatment patients) are receiving second or even third radiation therapy treatments with possibly varying fraction doses. Furthermore, new hypo-fractionations used in Stereotactic Body Radio therapy (SBRT) or Stereotactic Radio Surgery (SRS) are using much higher fraction doses than heretofore conventional treatments, making any non-linear effects caused from the biological effect of fraction dose (or the time between individual treatment sessions) more prominent.
The biological efficiency of a radiation dose is known to depend on the fraction dose (non-linear), treatment modality, energy, or even dose rate. In addition, different structures have different recovery rates from the delivered radiation dose. Also, other patient specific factors (e.g., background information, additional anatomical or functional information from PET scanning, response to previous treatments, etc.) can affect the predicted biological effect of certain amounts of dose.
One model that is commonly used to approximate the dependency between a biological effect and the dose delivered to tissue during a single fraction is the Linear-Quadratic (LQ) model, which takes into account non-linear effects due to the recovery rate of a structure. However, the LQ model does not take into consideration that the recovery rate may be different for different types of tissues. When treatments using different fraction doses are compared, it is common to convert the doses to a dose that would provide equal biological effect.
Unfortunately, in current DVH estimation techniques, these biological effects—such as different tissue recovery rates—are not taken into account. Typically, the total dose is considered just as a general scalar value of the dose distributions, and the fraction dose does not play a role at all in the calculation. Moreover, even if the treatment planner has accounted for the biological effect of different fractionations during the planning of the training set plans, current implementations do not properly account for the possibility that the planning goals are functions of the used fraction dose.